I work in a biochemistry lab and we often have to fit equations to data and determine the goodness of fit. In this set of slides I show a plotly plot that allows the user to zoom in on fitted data to determine how good the fit is.

• Our data involve protein to protein binding interactions
  • Understanding the nature of these interactions can aid in drug development
• If you are unfamiliar with 'binding', then think of it like taste. If you have a glass of tea and you add some sugar to it, small amounts of sugar will have only a little effect on the flavor. As you add more and more sugar eventually the tea will be sweet. If you add even more sugar, eventually the tea will be so sweet that you will hardly notice the difference as you add more sugar.
  • This mirrors what we see with molecular binding. As one binding partner is added, there is little change at first, then a large change in the % bound, followed by a saturation of binding.

This equation was used to fit the data on the next slide:
\(Fraction\ Bound = \frac{X}{K_a + X}\), whereby \(X\) is the concentration of a binding partner (the glucocorticoid receptor in this plot) and \(K_a\) is the affinity constant–an empirical value that describes how strongly two proteins interact.

The fitted \(K_a\) is 15.6 \(Molar^{-1}\).